Reports & Research

The insurance industry has been grappling for years with the skyrocketing losses caused by wildfires. As ZestyAI unveils its annual Wildfire Season Overview, we can see that insurers are in a pivotal position to navigate the ongoing threat.

Wildfire Risk Isn’t Going Anywhere

While we are currently experiencing a brief reprieve from the wildfire devastation of the last few years, the ongoing threat of wildfire remains at an all-time high.

Extreme snow and rainfall across the West in 2023 have led to wetter-than-normal conditions that have acutely reduced the risk of wildfire. However, wetter conditions lead to vegetation growth, so despite 2023 presenting lower wildfire risk, the resulting vegetation accumulation, combined with persistent drought conditions in future years, will likely result in extremely high losses in the coming years. In fact, heavy rainfall has preceded many of the most severe wildfire years ever recorded in California.

Heavy rainfall has preceded many of the most severe wildfire years ever recorded in California.

Preparing for Future Wildfire Seasons

With high wildfire activity on the horizon, what steps can insurance companies take now to prepare for future wildfire seasons?

Here are three essential strategies:

1. Leverage Data for Better Understanding

Research by ZestyAI reveals that wildfires ravage 87% more land during drought years compared to non-drought years. With the western US still experiencing a megadrought that is the worst in over a millennium, it’s critical to understand the data and risks involved.

Not all homes face high risk. For the remainder, detailed property risk insights can highlight areas requiring risk mitigation. Integrate property-specific wildfire risk data into the underwriting and renewal process. This year is also an excellent opportunity to review a complete portfolio using an AI-powered wildfire risk assessment tool like Z-FIRE.

2. Educate and Empower Property Owners Through Transparency

Technology, particularly satellite/aerial imagery and artificial intelligence, can shed light on wildfire risks. Insurers can use this technology to assess the risk reduction measures that policyholders have implemented and understand how a property might withstand a wildfire.

This information is invaluable for educating homeowners and insurance agents. By knowing the specific actions that can be taken to reduce risk, such as clearing brush or using fire-resistant materials, both insurers and homeowners can be better prepared for wildfires.

3. Choose a Technology Partner Wisely

ZestyAI's Z-FIRE has set a benchmark by integrating loss data from over 1,500 wildfires and employing cutting-edge technology to derive insights on each property. By combining aerial and satellite imagery with machine learning and cloud computing, ZestyAI created Z-FIRE, a highly detailed wildfire risk assessment model.

Z-FIRE has been adopted by leading insurance carriers in every single western US state.

In 2022, Z-FIRE demonstrated remarkable performance. Its integration of data through machine learning and computer vision models has established Z-FIRE as a potent tool in wildfire risk assessment for both underwriting and rating.

Make Informed Decisions with Z-FIRE

Using Z-FIRE, insurance carriers, MGAs, and reinsurers can get access to actionable insights developed from detailed property-level risk factors. While wildfire losses may be inevitable, understanding in detail how individual properties contribute to average and tail risks is a large step forward.

The specific time and location of a wildfire is nearly impossible to predict. However, Z-FIRE can give carriers an assessment of the preconditions for that fire, and describe in detail the factors which contribute to it. Knowing, not guessing, which properties fall into a high-risk category is more important now than ever. We look forward to helping our customers through this fire season and many to come.

Z-FIRE Stands Alone in Compliance

Z-FIRE has been developed in partnership with top carriers and has been included in successful filings in California and many other western states. As regulators continue to push for additional transparency and accuracy in how insurers treat wildfire risk, AI-powered solutions provide a clear advantage because of their interpretability and sensitivity to changing conditions.

In 2023, California began requiring insurers to provide discounts based on mitigation measures, and in 2024 Oregon is poised to establish similar requirements on communications to homeowners. All of these changes create a burden on insurers, but those who can adapt to the new regulatory environment by leveraging knowledgeable partners like ZestyAI will have an advantage over competitors. AI is part of the solution, helping address climate risk and maintaining the insurability of properties across the US.

Download ZestyAI's 2023 Wildfire Season Overview 

ZestyAI has released its annual Wildfire Season Overview for 2023. This comprehensive report combines insights from recent wildfire events, prevailing drought conditions, and cutting-edge advancements in artificial intelligence to assist insurers in effectively managing wildfire risk.

Download ZestyAI's 2023 Wildfire Season Overview

Here are some key findings from the report:

A Chance To Prepare While Wildfire Fuels Accumulate

Despite a brief respite from recent wildfire devastation, the current threat remains high. Over the past decade, wildfire risk has notably increased, particularly in California. However, the occurrence of extreme snow and rainfall in the West during 2023 has temporarily reduced the risk due to wetter conditions.

It's important to note that vegetation accumulation and ongoing droughts will likely lead to substantial losses in the coming years. California remains highly susceptible to losses and significant vegetation growth. This temporary relief in 2023 creates an ideal opportunity for insurers to review the risk technologies they have in place and embrace innovative solutions to prevent future losses.

No Role for Drought in Underwriting

Drought is indicative of fire intensity, but not losses. Although drought is an important factor in seasonal wildfire risk, the presence of drought shouldn't drive underwriting. Instead, insurers should look at property-specific solutions that consider wildfire risk over the lifetime of a policy.

Research has shown that this year's heavy rainfall may be a leading indicator for severe wildfire years to come. A comprehensive understanding of buildings, vegetation, and mitigation methods at the property level is necessary to effectively manage future wildfire risk.

A comprehensive understanding of buildings, vegetation, and mitigation methods at the property level is necessary to effectively manage future wildfire risk.

Using Advanced Models to Adapt to Changing Risks & Regulations

AI-powered risk models play a key role in mitigation. Insurers who write business in wildfire states have found increasing value in AI-powered wildfire risk models as they offer actionable risk insights, adapt quickly to changing climate risks, and comply with all regulations.

Over the last year, several western states have begun to implement new regulations for insurers in response to the changing risk environment. Discounts and transparency for mitigation efforts and property-specific decisions may become an industry standard as they have in California and Oregon.

What This Means for Insurers

In evaluating wildfire risk, many analyses tend to focus on the number of fires and the size of the area they burn. However, what really matters to insurance companies and property owners is the loss of structures and what can be done to mitigate those losses.

For example, those providing insurance in California might be surprised to learn that despite smaller losses in 2022 compared to 2021, the total national count of acres burned and fires ignited in 2022 actually exceeded that of 2021. This mismatch between yearly wildfire activity and the number of structures lost suggests that wildfire losses are not simply dictated by wildfire activity.

The most significant factor is not how many fires start, or how far they spread, but the potential resilience of every structure and what the communities and homeowners have done to prepare for wildfire exposure. Research from ZestyAI and IBHS shows that for a more precise understanding of potential losses, insurers need to zoom in on individual properties. They should consider a structure’s location, building materials, surrounding vegetation, and efforts taken by the surrounding community to prepare for wildfires.

Modern wildfire risk tools like ZestyAI's Z-FIRE do just that. They analyze individual property features and measure the impact of those features on the probability of loss. They also factor in nearby vegetation, community preparations, local infrastructure, and the lay of the land. This property-centric approach doesn’t try to predict exactly what a wildfire will do. Instead, it gives valuable information on how and why properties might be damaged by wildfires.

These models don't just offer a simple risk score, but also help explain what makes a particular property vulnerable and what steps can be taken to protect it.

 
Find out more, including how Z-FIRE performed in 2022, in this year’s Wildfire Season Overview.

Download ZestyAI's 2023 Wildfire Season Overview

San Francisco, CA, April 19, 2023 – Today ZestyAI, the leading provider of climate and property risk analytics solutions powered by artificial intelligence (AI), and the Insurance Institute for Business & Home Safety (IBHS) released new research examining catastrophic losses from severe convective storms, particularly hail. The study focuses on hail-driven losses in property and casualty insurance.  

Hail losses are a persistent problem for property insurers’ risk management efforts. Historically, carriers have focused on intense events to predict hail risk, with supporting data confined to storms with hailstones larger than one or two inches. The study Small Hail, Big Problems, New Approach shows high concentrations of small hail are more important than previously thought, pointing to an opportunity to broaden data sets to account for the cumulative effect all hailstorms have on a roof’s susceptibility to damage over time, leading to a claim. 

This new research shows all hail needs to be accounted for when modeling and ultimately understanding losses. Using data from all hail events, not just those with hail that meet the severe criteria of one inch or greater, allows carriers to consider valuable data on smaller hailstone impacts. Additionally, insurers can integrate climate and materials science to better understand hail frequency and severity. Research suggests using this new approach could perform as much as 58 times more accurately than looking at events with large and very large maximum hail sizes alone, allowing carriers to more effectively assess hail risk, achieve more profitable underwriting and open up ratings to previously avoided areas.  

“As we’ve learned more about hailstorms, we've discovered storms that produce large concentrations of small hail are more common than we thought, and despite causing less individual damage than a single large hailstone, small hail, especially in high concentrations, is likely a meaningful contributor to the loss we see each year from hail,” said Dr. Ian Giammanco, managing director of standards and data analytics at IBHS. “Experiments also show large concentrations of smaller hailstones cause degradation to the asphalt shingles, specifically dislodging large amounts of granules. Once enough granules are lost, the underlying asphalt material can become more susceptible to aging and weathering. Repeated exposure to these types of hailstorms can shorten the life of an asphalt shingle roof and increase the damage caused by large hailstones in the next storm.” 

“Hail losses are a persistent problem for property insurers’ risk management efforts,” said Attila Toth, founder and CEO of ZestyAI. “Three of the nation’s five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem.” 

“Three of the nation’s five largest publicly-traded P&C carriers mentioned hail as a key concern in 2022 financial reports. Greater losses have brought attention to hail risk, and the insurance industry needs better approaches to solve this problem.” 

Hail risk can be especially costly to insurers because, unlike other catastrophic perils like hurricanes and wildfires, it can be difficult to identify the storm that caused a hail claim. As a result, insurance carriers could be forced to raise overall premiums or introduce high deductibles to compensate for the added costs.

As climate and materials science have developed, more data has become available providing  improved hail risk evaluation options that can lead to better decisions at earlier stages of the policy life cycle. Other benefits could include more profitable underwriting, a greater ability to rate previously-avoided areas and significantly reduced loss ratios.

For the complete ZestyAI and IBHS research paper visit this page.
 

About ZestyAI

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information. 

About the Insurance Institute for Business & Home Safety (IBHS)

The IBHS mission is to conduct objective, scientific research to identify and promote effective actions that strengthen homes, businesses and communities against natural disasters and other causes of loss. Learn more about IBHS at ibhs.org. 

### 

For more information, contact:

Linsey Flannery

Director of Communications, ZestyAI 

linsey@zesty.ai 

416-939-9773 

Mary Anne Byrd

Communications Director, IBHS

mbyrd@ibhs.org

803-669-4216 

Reinsurance rates are spiking to an all-time high. Fitch estimated a 20-60% rate increase for cedants in the overall property reinsurance market at the January 1st renewals.¹ Terms and conditions are also tightening - many reinsurers are limiting their cedants to much higher attachment points², or exiting CAT-exposed lines altogether

The main drivers for uptick in reinsurance rates

Our research has found three drivers underpinning the trend:

1. Devastating CAT losses, particularly from secondary perils

59% of all CAT losses come from secondary perils³, and those losses have caused major shifts in the reinsurance landscape. Howden estimates that global property CAT reinsurance rates were up 37% at the January renewals⁴.

2. A new urgency to improve return on capital

“When the cost of capital is equal to the rate of return, something has to change.” - Aditya Dutt, CEO of Aeolus Capital Management⁵. The reinsurance industry has underperformed since 2017, with an average return on equity of just under 5%⁶. Poor underwriting performance was a key driver, with an industry average 101% combined ratio over the same period⁷. Reinsurers are poised to use the tightening market as a chance to improve performance, with Fitch forecasting a 4pp underwriting margin expansion for reinsurers in 2023⁸. Unfortunately for primary insurers, Goldman Sachs predicts that the same tightening market will create significant volatility for cedants⁹.

3. Value erosion in reinsurer investment portfolios

Macroeconomic factors are driving significant unrealized investment losses for reinsurers, particularly on fixed income portfolios due to rising interest rates. Aon estimates that these investment portfolio losses drove a 17% decline in global reinsurance capital across the first 9 months of 2022, with some players reporting equity value losses as high as 40-50% over that period¹⁰. Reinsurers will look to shore up these losses with better underwriting performance, which likely means tougher rates for primary carriers.

How property insurers can improve their odds with AI-powered predictive climate and property risk platforms

These factors mean that primary insurers can expect challenging reinsurance negotiations at the June 1st renewal deadline, particularly on property lines. However, new AI-powered predictive climate and property risk platforms can improve the odds for property insurers in three areas:

1. Rapid improvements in risk mitigation

Implementation-free portfolio reviews can quickly drive major loss ratio improvements.

2. Turn the tables of CAT risk screening in your favor

Improving data quality can lead to more favorable stochastic model portfolio screens, particularly with insight about the roof.

3. Enter the room as a leader in cutting-edge risk practices

Showing the same commitment to new technologies as industry leaders can help cedants build a better case.

Conclusion

With the right mitigation action and a cutting edge view of portfolio risk, cedants can navigate the upcoming 6/1 renewal successfully.

Learn more about how an AI-powered predictive climate and property risk platform can help you.

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Sources

1 & 8 - Fitch, Reinsurers’ Underwriting Margins to Expand by 4pp in 2023

2 & 3 - Gallagher Re, Gallagher Re Natural Catastrophe Report 2022

4 - Howden, Howden’s renewal report at 1.1.2023: The Great Realignment

5 - AM Best, Reinsurance: Roundtable Discussion on Renewals and What 2023 May Hold

6, 7 & 10 - AON, Reinsurance Market Dynamics

9 -  Reinsurance News, Hard market to increase volatility for primary insurers: Goldman Sachs

San Francisco, CA, September 20, 2022 – ZestyAI, the leading provider of property risk analytics solutions powered by Artificial Intelligence (AI), has developed a 180-day playbook to support insurance carriers as they work to meet the Mitigation in Rating Plans and Wildfire Risk Models regulation expected to be adopted by the California Department of Insurance (CDI) before year-end. The playbook reflects the company’s unique ability as the only comprehensive solution in the marketplace to help insurers meet or exceed every single requirement in the new regulation — meeting 100 percent compliance inside the tight 180-day window.

On September 7, 2022, Insurance Commissioner Ricardo Lara announced he had submitted the department’s insurance rating regulation recognizing wildfire and safety mitigation efforts made by homeowners and businesses, to the California Office of Administrative Law for final approval. This first-of-its-kind regulation will require all insurers in California to refile their existing rating plans on an aggressive 180-day timeline. 

“Eight of the ten most destructive wildfires in California’s history have occurred in the last five years,” said Attila Toth, Founder and CEO of ZestyAI. “While the new wildfire regulations will have a significant impact on California’s insurance industry, adapting to this peril is key to having a sustainable insurance ecosystem in California. As the leader in property-specific wildfire risk assessment, we have offered input at each step of this process. We are here to support admitted carriers with a turnkey solution complying with every single requirement as they navigate this process and work to meet the new regulations.”

The new wildfire safety regulation requires insurance companies to consider the structure of a home, its surroundings, and community-level mitigation. Insurers with concerns about the regulation can reach out to ZestyAI to get a complete explanation of how the regulations will impact them. This includes access to the 180-day playbook, which breaks down the regulatory compliance process into an orderly roadmap that addresses all three major challenges that insurers will face:

• Operational — The process of rapidly integrating new data sources, educating the public on how wildfire mitigation affects insurance policies, and a framework for a compliant appeals process.
• Rating — How to weight property-specific characteristics, including those with and without historical loss data, in rating plans as well as guidance on mitigation credits.
• Filing — Carriers who use a rating plan reliant on traditional wildfire models without property-specific information will need to overhaul their rating framework. Relying on multiple approved rate filings, ZestyAI has developed a comprehensive filing toolkit that can support carriers at every facet of the filing process.

ZestyAI’s Z-FIRE™ model has quickly become the leader in property-specific wildfire risk assessment. Using AI algorithms trained on more than 1,500 wildfire events across 20 years of historical loss data, Z-FIRE™ provides a level of detail that is of essential value to both the insurer and the homeowner.

The model was the first AI model ever approved as part of a rate filing by the CDI and the second wildfire risk model. It has been widely adopted across the Western U.S., where its use has been approved for both underwriting and rating. During 2021's APCIA Western Region Conference, CDI representatives expressed that the agency’s familiarity with Z-FIRE™  means in future filings the focus will be limited to the carrier's specific use of the model, not the details of the model itself, potentially greatly expediting the reviews of carriers using the Z-FIRE™ model.

ZestyAI’s Z-FIRE™ considers features such as topography and historical climate data in combination with factors extracted from high-resolution imagery of the property itself and its surroundings, including homeowner and community mitigation efforts, to provide both neighborhood and property-specific risk scores. 

A significant advantage to insurance carriers is that they can use these data elements to communicate with homeowners on what specific actions can be taken to lower their property’s risk, such as upgrading building materials and cutting down surrounding dry brush or overhanging vegetation. The impact of mitigation efforts can be significant. A joint study by the Insurance Institute for Business & Home Safety (IBHS) and ZestyAI, which studied over 71,100 wildfire-exposed properties, found that property owners who clear vegetation from the perimeter of their home or building can nearly double their structure's likelihood of surviving a wildfire.

About ZestyAI

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in the United States. The company uses AI, including computer vision, to build a digital twin for every building across the country, encompassing 200 billion property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit zesty.ai for more information.

Download Report

Today, ZestyAI released its 2022 Wildfire Season Overview. Each year, ZestyAI prepares a comprehensive overview to help guide insurers based on recent wildfire events, persistent drought conditions, and advancements in artificial intelligence for managing wildfire risk.

If it seems like wildfires are burning at all times of the year, it's not just you. Very destructive events, like last December's Marshall Fire, are occurring in months not typically associated with high wildfire danger. Those who study wildfires, including ZestyAI, have begun to start thinking in wildfire "years" instead of wildfire "seasons'. Strong wildfire years, with 10+ million acres burned, have quickly become the new normal. The last 10 years have been the worst on record for property and casualty (P&C) insurers when it comes to wildfire. 8 of the top 20 fires in California history, and more than half of the acreage burned by them, occurred in just the years 2020 and 2021.

What can insurers do to prepare themselves for persistent wildfires?

• Understand the Data: Instead of sticking with decades-old approaches, assess wildfire risk at the property level.
• Continue to Bring Transparency and Education to Homeowners: Insights from AI-based wildfire risk models may be passed on to homeowners and agents, enabling a much better understanding of wildfire risk.
• Find the Right Technology Partner: Aerial and satellite imagery, machine learning, and infinitely scalable cloud computing resources were combined to build the most granular wildfire risk assessment model (Z-FIRE™). Using Z-FIRE™, ZestyAI can accurately estimate an individual property’s wildfire risk, plus highlight the key property-level factors that contribute to that risk.

Click here to download ZestyAI's 2022 Wildfire Season Overview.

ZestyAI offers insurers and real estate companies access to precise intelligence about every property in North America. The company uses AI, including computer vision, to build a digital twin for every building in North America, encompassing 200B property insights accounting for all details that could impact a property’s value and associated risks, including the potential impact of natural disasters. Visit https://zesty.ai for more information.

We’re proud to share that ZestyAI has been named to Sønr’s 2025 Beyond Boundaries Scale50, recognizing the top 50 established insurtechs driving measurable impact and transformation across the global insurance industry.

Produced by Sønr, a leading market intelligence firm tracking more than four million companies worldwide, the Beyond Boundaries 2025 report identifies the innovators redefining insurance through AI, data, and collaboration.

This year’s analysis underscores a clear shift in the market: the age of experimentation has given way to execution and scale—where efficiency, resilience, and real-world outcomes define success.

At ZestyAI, we’re proud to be part of that evolution. Our Decision Intelligence Platform brings together property-level data, predictive AI models, and Agentic AI automation to help insurers see, price, and manage risk with precision and confidence.

Trusted by carriers and regulators across the U.S., ZestyAI’s solutions deliver measurable improvements across underwriting, rating, reinsurance, and regulatory workflows—helping insurers make faster and more data-driven decisions.

Matt Connolly, Founder and CEO of Sønr, said:

The insurance industry has long talked about change. And now, we’re seeing it happen. After years of incremental steps, the market is finally embracing the opportunities technology brings - and the impact is tangible.

Read the full report: Beyond Boundaries 2025

DUAL North America Inc.’s (“DUAL”) personal property division has selected ZestyAI’s Z-STORM™ model to enhance storm-risk underwriting and pricing as it continues its rapid US expansion.

The partnership equips DUAL with sharper risk differentiation, more accurate underwriting and pricing, and a stronger foundation for sustainable growth in regions increasingly affected by severe convective storms.

By adopting ZestyAI’s severe convective storm model, DUAL will strengthen its ability to identify and price the combined effects of hail and wind with greater precision. This will enable faster, more informed decisions and profitable expansion while maintaining regulatory compliance.

The collaboration reflects DUAL’s continued investment in advanced analytics and technology to support long-term growth.

The specialty program administrator, offering more than 40 insurance products and surpassing $1.3 billion in gross written premium in 2024, continues to broaden its capabilities across commercial, specialty, and personal lines.

Luke Wolmer, Chief Actuary at DUAL, said:

“As we continue to grow across personal property lines, having accurate risk prediction at the property level is crucial."

Z-STORM gives us a more nuanced understanding of storm vulnerability, helping us recognize differences in risk that traditional models overlook. This enhances our team’s confidence in pricing decisions and will support our continued expansion across the U.S.”

Z-STORM is an AI-powered risk model that evaluates the combined effects of hail and wind to predict the frequency and severity of storm-related damage at the property level. By analyzing the interaction between local climatology and the unique characteristics of every structure—including roof condition, material, and surrounding exposure—the model delivers precise, property-specific insights into storm vulnerability.

In September 2025, ZestyAI introduced mitigation-aware scoring to its severe convective storm suite, allowing insurers to dynamically adjust risk scores to reflect verified improvements such as roof replacements, upgraded materials, or corrected property data. This enhancement gives carriers a scalable way to recognize mitigation within pricing and underwriting workflows, advancing transparency and regulatory alignment.

Attila Toth, Founder and CEO of ZestyAI, said:

“DUAL’s adoption of Z-STORM reflects a forward-thinking approach to storm risk management."

"By applying property-level risk analytics and mitigation-aware scoring, DUAL is positioned to underwrite more precisely, grow responsibly, and strengthen community resilience across the regions that are most exposed to extreme weather”.

ZestyAI’s storm models are regulatory reviewed and ready to use across the Great Plains, Midwest, and U.S. South, regions most impacted by severe convective storms, and are actively used by carriers for rating and underwriting.

ZestyAI today announced the expansion of ZORRO Discover™ to all property and casualty insurance lines.

ZORRO Discover analyzes millions of state filings to surface real-time regulatory and market intelligence, giving carriers actionable insights to make faster, more confident decisions. Carriers using the platform have reduced adverse selection, accelerated regulatory approvals by up to 50%, and expanded analytical capacity more than 20-fold—turning what was once a manual, fragmented process into a source of strategic advantage.

The platform now delivers unified visibility across all P&C lines, including Commercial Auto and Property, Personal Auto and Property, Financial and Specialty Lines, Liability and Professional Lines, Workers’ Compensation, and Administrative filings—covering every major filing type across the United States.

Built on ZestyAI’s Agentic AI platform, ZORRO Discover scales decision intelligence across the insurance industry, transforming over a decade of U.S. insurance filings into a single, transparent system of insight. Carriers can instantly benchmark competitors, analyze rating trends, and anticipate regulator feedback and objections in real time, turning regulatory filings from a compliance requirement into a strategic advantage.

Kumar Dhuvur, Chief Product Officer and Co-Founder at ZestyAI, said:

“Every corner of P&C faces the same challenge: too many filings and too little time. Now, whether it’s workers’ comp in Texas or commercial auto in California, teams can simply ask ZORRO and get instant, verified insights in real time.”

By analyzing past objections and outcomes, teams can anticipate regulators’ questions before they arise and move filings forward with precision. Live monitoring of new submissions keeps organizations current on competitor moves and market shifts, turning what was once a fragmented, manual workflow into a real-time decision system that helps teams act quickly and strategically.

With its conversational interface, users can simply ask ZORRO to surface insights that once took hours or days to uncover. Product, actuarial, and regulatory teams can now collaborate from a single, auditable source of truth, replacing manual searches and static spreadsheets with transparent, explainable intelligence that drives faster, smarter action.

ZORRO Discover is available now for all property and casualty insurance lines.

Start your trial.

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At ZestyAI, we know that better data leads to better decisions. That’s why we’ve invested in a major upgrade to our Roof Age product, trusted by leading carriers to improve risk selection, pricing, and operational efficiency in property insurance.

Today, we’re excited to share what’s new, what’s improved, and how these advancements are already helping carriers strengthen underwriting, rating, and inspection workflows.

What’s New in Roof Age

We’ve taken a holistic approach to improving performance, accuracy, and efficiency. Here’s what you’ll find in the latest release:

Refit model with double the training data

We’ve significantly enhanced the Roof Age model, doubling the size of our training dataset to improve performance across diverse housing stock, roof types, and geographies.

This expanded dataset incorporates more confirmed roof replacement events and broader regional variation, allowing the model to generalize more effectively to different parts of the country, including historically underrepresented regions.

The model is now better able to distinguish between full roof replacements and other types of roof-related activity, such as solar panel installation, patched sections, partial replacements, or home additions.

These events may alter the roof’s appearance or condition, but don’t represent a comprehensive replacement. By learning the subtle visual and contextual cues that separate these scenarios, the model delivers more accurate predictions and reduces the risk of misclassification.

Enhanced estimation for challenging cases

In cases where no building permit is available and roof replacement can’t be clearly confirmed via aerial imagery, our improved Roof Age Estimation Model takes over. This model, now trained on double the dataset, is purpose-built for ambiguity.

It leverages not only imagery and property-level features but also regional climatology, using knowledge of local weather patterns and environmental stressors to inform its estimate.

For example, a roof in the Southeast exposed to intense sun and humidity will age differently than one in the Pacific Northwest or Upper Midwest. Incorporating these regional factors helps improve estimation accuracy, even when direct replacement signals are unavailable.

ZestyAI also establishes a minimum roof age, providing additional clarity and confidence. Using our extensive, 20-year aerial imagery catalog, we can identify the earliest visual evidence of the current roof.

If no replacement activity is detected over a known span of time, we can confidently assert that the roof is at least that old.

This minimum age is then used not just as a floor, but as a valuable input to further refine the overall roof age estimate, narrowing the prediction with greater precision than models limited to single-source or snapshot data.

This capability provides underwriters and actuaries with a powerful, high-confidence signal, particularly valuable for pricing segmentation, inspection prioritization, and risk selection strategies.

Intelligent cross-validation logic

The model doesn’t rely on a single data source. Even when a strong signal like a building permit is available, it cross-validates with high-resolution aerial imagery to detect inconsistencies, like permits that were filed but not followed through, or replacements that occurred without permits.

This layered logic helps ensure predictions are grounded in current conditions, not just administrative records. It also improves detection of fraud, data entry errors, or outdated assumptions in property records.

This logic creates a "trust but verify" framework that boosts both precision and confidence in every prediction.

To illustrate, imagine a home built 12 years ago. The model begins by anchoring to the construction year, then scans forward through our aerial imagery catalog and permit records to assess whether a roof replacement has occurred.

By grounding the analysis in the property's timeline, the model avoids misinterpreting the original roof as a newer installation and increases confidence in identifying true replacement events.

Expanded imagery catalog

We’ve enriched our aerial imagery sources to improve roof verification across geographies. The result: more accurate verification of roof replacements and improved model performance in hard-to-cover geographies.

This helps carriers score more properties with higher confidence, especially in rural or previously under-covered regions.

Confidence scores for every prediction

Every Roof Age prediction now comes with a confidence score, helping carriers make more informed decisions. High-confidence predictions can be fast-tracked for automated processing, while lower-confidence scores can trigger secondary review or inspection.

This added transparency empowers carriers to make risk-based decisions not only on the prediction itself, but on how much to rely on it.

Improved Performance Behind the Scenes

We’ve also made significant infrastructure upgrades to enhance product speed and reliability.

• Reduced Latency: Infrastructure improvements have cut average response times to under 2.5 seconds per property, making Roof Age a real-time-ready solution for quoting and policy decisions.
• Stricter Quality Controls: We’ve added new safeguards to filter out imagery that’s blurry, outdated, or contains visual artifacts. Only high-resolution, high-confidence inputs are used to power predictions.
• Scalability: These backend improvements also allow us to handle larger portfolios with more concurrent requests. This is ideal for carriers integrating Roof Age into enterprise systems.

Easier Access for Every Workflow

Roof Age is available wherever you need it:

• In Z-VIEW: Easily visualize Roof Age predictions and supporting evidence with property-level insights directly in our web application.
• Via API: Seamlessly score entire portfolios and integrate directly into your quoting, pricing, or inspection strategies.

Ready to See the Results for Yourself?

The feedback from the market has been tremendous, and we’re just getting started. Want to see the results for yourself? We’re inviting carriers to pilot the new Roof Age model and evaluate its performance on their own book of business.

Get in touch to schedule your Roof Age pilot

ZestyAI announced that Brava Roof Tile, a leader in premium synthetic roofing solutions backed by Golden Gate Capital, has selected ZestyAI to validate the real-world performance of its roofing systems during past storms.

How Brava Roof Tile Uses ZestyAI’s Property and Roof Intelligence

Brava Roof Tile is leveraging three of ZestyAI’s proven solutions to bring greater clarity to roof performance and replacement opportunities. Roof Age synthesizes building permit data with 20+ years of high-resolution aerial imagery, applying advanced machine learning to deliver verified roof age estimates with 97% U.S. coverage.

Within Z-PROPERTY™, Digital Roof applies AI to assess roof complexity, materials, and condition, flagging vulnerabilities before they become costly failures, while Location Insights evaluates the broader parcel to surface risk factors such as vegetation overhang, yard debris, and secondary structure.

Together, these insights provide comprehensive coverage, unmatched accuracy, and fast deployment at scale, turning property-level data into actionable guidance on roof vulnerabilities and replacement opportunities.

Validating Real-World Resilience With Property-Level Data

“Brava is committed to helping homeowners protect their most valuable asset with roofs that combine durability, sustainability, and beauty,” said Matt Pronk, Chief Financial Officer of Brava Roof Tile.

“With ZestyAI, we gain a clear, data-driven view of how roofs perform in the real world and use those insights to guide families toward stronger, longer-lasting protection.”

“Brava Roof Tile is showing how ZestyAI's risk analytics can be applied to validate resilience in the real world,” said Attila Toth, Founder and CEO of ZestyAI.

“Our mission is to protect families, communities, and their financial wellbeing, and our unmatched coverage and accuracy make that possible at scale."

At ZestyAI, we’re often asked:
What Does “Property-Level” Mean? 
What Makes ZestyAI Different from Traditional Risk Models?
Are ZestyAI Models Approved for Use in Underwriting and Rating?

This post answers the most common questions we receive from underwriters, actuaries, regulators, and technology partners—using wildfire, hail, wind, water, and storm perils as examples of how we turn complex data into actionable insights.

What Does “Property-Level” Mean?

Traditional risk models often rely on ZIP codes, territories, or broad regional averages to assess hazard and vulnerability. Stochastic models may support property-specific analysis, but they typically require external data sources, which adds cost, complexity, and inconsistency.

At ZestyAI, we assess each structure based on its physical characteristics and how it interacts with the surrounding environment.

We assess:

• Parcel boundaries and building footprints
• High-resolution aerial and oblique imagery
• Topography, slope, and vegetation
• Structural details like roof shape, materials, and defensible space

By integrating climatology with the built environment, we generate contextual risk scores that capture how each property’s physical characteristics, regional climatology, and historical loss experience interact to shape real-world risk.

That drives smarter decisions in underwriting, pricing, and mitigation, without relying on assumptions or manually sourced data.

What Makes ZestyAI Different from Traditional Risk Models?

Traditional risk tools often rely on:

• Broad hazard zones that can’t distinguish risk within a ZIP code
• Infrequent model updates that fail to reflect current conditions
• Over-simplified proxies—often relying solely on factors like year built—without accounting for deeper structural nuance
• Manual inspections that are slow, inconsistent, and costly

ZestyAI takes a fundamentally different approach.

Our models:

• Use gradient boosted machines that capture complex interactions between property features and environmental conditions
• Are trained on millions of actual insurance claims, not simulations, ensuring outputs reflect real-world loss experience
• Leverage both imagery (e.g., high-resolution aerial and oblique photos) and non-imagery sources (e.g., permits, climatology, topography)
• Continuously incorporate new data to reflect changing exposures
• Provide parcel-level risk scores with full transparency and regulator-ready documentation

With 97%+ U.S. property coverage, ZestyAI delivers national models with localized precision, helping carriers segment risk, price accurately, and respond to today’s evolving climate risks.

Are ZestyAI Models Approved for Use in Underwriting and Rating?

Yes. ZestyAI’s models, including Z-FIRE™, Z-HAIL™, Z-WIND™, Z-WATER™, and Z-STORM™, have been approved for use in underwriting and rating across the U.S.

Our regulatory approach is grounded in a few key principles:

• Transparency: We provide clear, regulator-ready documentation, including model methodology, variable selection rationale, and statistical validation.
• Collaboration: We work directly with carriers and state regulators throughout the filing process, from pre-submission briefings to objections.
• Responsible Innovation: Our models are trained on real-world claims, regularly updated with new data, and built with fairness and explainability in mind.

We support filings with:

• Detailed methodology and input documentation
• Variable importance rankings and validation studies
• Pre-built regulatory summaries to streamline the review
• Ongoing support throughout the regulatory lifecycle

ZestyAI has a track record of success navigating regulatory review. We help carriers adopt cutting-edge risk models with confidence and compliance.

Roof Age vs. Roof Condition: What’s the Difference?

ZestyAI’s models distinguish between roof age and roof condition, treating them as complementary signals that together provide a more accurate picture of roof-related risk, especially for hail and wind.

Roof Age is validated using a combination of building permit data and aerial imagery, analyzed through multiple proprietary methods simultaneously. We assign confidence scores to each roof age and apply minimum roof age rules to avoid false positives, ensuring the data is robust, even in jurisdictions with limited permitting records.

Roof Condition is assessed through computer vision models applied to high-resolution aerial imagery. These models detect visual signs of degradation, such as discoloration, wear, patching, and debris, that may not correlate with official replacement dates.

Why does this matter?

Because many insurers rely solely on reported roof age, which is often missing, outdated, or self-reported.

Our approach captures:

• Properties with older roofs that are still in good shape (and may be lower risk)
• Properties with newer roofs already showing signs of wear (and may be higher risk)
• Up-to-date roof vulnerability that static datasets can’t match

Together, roof age and condition power smarter decisions in underwriting, pricing, and mitigation—grounded in observable reality, not assumptions.

Modeling Approach: Why Gradient-Boosted Machines (GBMs)?

ZestyAI's risk models use gradient boosted machines (GBMs), a machine learning technique that delivers powerful predictive performance while remaining transparent and regulator-ready.

We use GBMs because they:

• Achieve high predictive accuracy by combining many simple decision trees into an ensemble that learns from its own errors over time, ideal for capturing complex insurance risk signals.
• Model non-linear interactions between variables, such as how roof complexity, condition, and regional climatology jointly influence risk, something traditional models or GLMs often miss.
• Enable transparency and explainability, with tools like feature importance rankings, partial dependence plots, and SHAP values that help underwriters, actuaries, and regulators understand what’s driving risk scores.
• Support a wide range of input types, from imagery-derived features to structured data like permits, topography, and property characteristics, all in one unified framework.

The result: a modeling approach that delivers real-world impact, supporting smarter underwriting, better pricing, and confident regulatory adoption.

How Z-FIRE Evaluates Wildfire Risk

Z-FIRE is ZestyAI’s structure-level wildfire risk model, built to capture both traditional wildland fire exposure and the growing threat of urban conflagration. Unlike traditional hazard maps that apply uniform risk zones across ZIP codes or counties, Z-FIRE delivers granular, property-specific risk scores for every structure in the U.S.

The model includes two levels of scoring:

1. Level One: Exposure Risk – Evaluates how likely a structure is to fall within a future wildfire perimeter, based on vegetation, slope, elevation, proximity to the wildland-urban interface (WUI), historical burn patterns, and regional climatology.
2. Level Two: Structure Vulnerability – Assesses how likely that structure is to be damaged if a wildfire occurs nearby. This score factors in structural characteristics like building materials, defensible space, and surrounding fuels, extracted from aerial imagery using computer vision.

Together, these scores provide a more complete view of wildfire risk: not just where fires may happen, but how individual structures are likely to perform.

Z-FIRE also captures non-traditional wildfire scenarios, including embers and wind-driven fires that jump the WUI and ignite dense suburban and urban neighborhoods. This makes the model particularly valuable for identifying concentration risk, urban conflagration, and managing PML across books of business.

Z-FIRE is validated on millions of insurance claims and performs reliably across all geographies—from the forests of California and the grasslands of Texas to emerging risk zones in Colorado, Oregon, and the Eastern U.S.

How Z-HAIL Accounts for Roof Vulnerability

Z-HAIL is a property-specific hail risk model designed to assess not just the likelihood of hail, but how damaging it will be to a specific structure.

Unlike traditional models that rely on historical hail frequency alone, Z-HAIL captures the interaction between local climatology and structural resilience by analyzing:

• Hail climatology: storm frequency, hailstone size, and intensity at a hyperlocal level
• Roof geometry and materials: pitch, complexity, covering type, and other features that influence how hail impacts a roof
• Property-specific vulnerability factors: including building height, exposure, and roof condition (derived from imagery and computer vision)

By modeling how hail behaves in a given location and how a specific roof is likely to perform under those conditions, Z-HAIL delivers precise risk segmentation at the parcel level. 

Carriers using Z-HAIL have seen significant improvements in underwriting performance. In an independent third-party review, Z-HAIL demonstrated a 20× lift in loss ratio segmentation between high- and low-risk properties—enabling more accurate pricing, better risk selection, and actionable mitigation strategies.

How Z-WIND Analyzes Wind-Driven Damage

Z-WIND is a property-specific model that evaluates vulnerability to both straight-line winds and tornadic activity by analyzing how wind climatology interacts with structure-level characteristics.

The model captures:

• Roof geometry: including shape, pitch, and surface area, which influence uplift forces
• Building elevation: to assess exposure to wind at various heights
• Local terrain and land cover: which impact wind speed, turbulence, and exposure to flying debris
• Historical wind climatology: including storm frequency and intensity
• Real-world claims data: to ensure outputs reflect actual loss performance

Z-WIND generates property-level frequency and severity scores, helping insurers move beyond broad wind zones to more precisely identify risk at the structure level. By understanding how specific buildings respond to local wind conditions, Z-WIND enables more accurate pricing, underwriting, and mitigation strategies across both inland and coastal regions.

How Z-WATER Tackles Non-Weather Water Losses

Z-WATER is an AI-powered model that predicts the frequency and severity of non-weather water and freeze claims at the property level, covering every structure in the contiguous U.S. 

While many traditional models depend heavily on basic indicators such as “year built,” Z-WATER combines those inputs with a broader set of property, climate, and infrastructure features to capture the interaction between three core dimensions of risk:

• Construction & Architecture: Property-specific features that influence vulnerability and claim severity, such as number of bathrooms, number of stories, presence of a pool, and overhanging vegetation (a signal for potential tree root intrusion).
• Climatology: Environmental stressors like temperature swings and freeze/thaw cycles that contribute to pipe bursts and system strain.
• Local Infrastructure & Hydrology: How local plumbing systems and electrical grids perform when real-world cold snaps or heat waves exceed what regional codes anticipated, exposing systemic weak points that lead to burst pipes and interior water damage.

These variables are derived from aerial imagery, tax assessment data, and regional climate and infrastructure datasets, all processed through ZestyAI’s proprietary AI framework.

Z-WATER helps insurers:

• Set fair and adequate rates based on true exposure
• Target high-risk homes for mitigation (e.g., water sensors or shutoff valves)
• Streamline operations by automating low-risk decisions and focusing resources where they matter most

What Is Z-STORM?

Z-STORM is a predictive, property-specific model built for carriers that rate hail and wind as a combined peril. It provides structure-level risk scores across the contiguous U.S., enabling more accurate pricing, underwriting, and mitigation decisions.

Unlike traditional territory-based approaches, Z-STORM models how storm climatology interacts with the built environment—capturing the real-world conditions that drive loss at the individual property level.

The model incorporates:

• Storm climatology: frequency and severity of wind and hail events at a hyperlocal scale
• Structural features: roof shape, material, pitch, and condition—key factors in a structure’s vulnerability
• Environmental context: including open terrain and nearby vegetation, which can amplify damage

Z-STORM predicts both:

1. Claim frequency: the likelihood a property will experience a storm-related claim
2. Claim severity: the expected loss as a percentage of Coverage A, providing a more precise view of financial exposure

This dual prediction enables carriers to:

• Accurately rate combined wind and hail risk at the property level
• Target mitigation strategies (e.g., roof improvements that reduce exposure to both hazards)
• Improve risk segmentation and pricing

Z-STORM offers a single, AI-powered solution for capturing the true complexity of convective storm risk—from climate data to construction detail to expected loss outcome.